From edbccdfcaf46f11e631afe98796f3e6e170da5d0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 26 Oct 2014 05:04:34 +0000
Subject: [PATCH] Maybe something changed?
---
src/convolutional_layer.c | 204 +++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 181 insertions(+), 23 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 2d4d748..1587ae8 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -2,6 +2,7 @@
#include "utils.h"
#include "mini_blas.h"
#include <stdio.h>
+#include <time.h>
int convolutional_out_height(convolutional_layer layer)
{
@@ -147,15 +148,9 @@
for(i = 0; i < layer.batch; ++i){
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
- c += n*m;
- in += layer.h*layer.w*layer.c;
b += k*n;
+ c += n*m;
}
- /*
- int i;
- for(i = 0; i < m*n; ++i) printf("%f, ", layer.output[i]);
- printf("\n");
- */
activate_array(layer.output, m*n*layer.batch, layer.activation);
}
@@ -201,14 +196,15 @@
b = layer.delta;
c = layer.col_image;
- memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
-
for(i = 0; i < layer.batch; ++i){
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
- col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
- c += k*n;
- delta += layer.h*layer.w*layer.c;
+ b += k*n;
+ c += m*n;
}
+
+ memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+
+ col2im_cpu(layer.col_image, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
}
}
@@ -216,7 +212,7 @@
{
int size = layer.size*layer.size*layer.c*layer.n;
axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.n,layer.momentum, layer.bias_updates, 1);
+ scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
scal_cpu(size, 1.-layer.learning_rate*layer.decay, layer.filters, 1);
axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
@@ -278,22 +274,184 @@
}
#ifdef GPU
+
+cl_kernel get_convolutional_learn_bias_kernel()
+{
+ static int init = 0;
+ static cl_kernel kernel;
+ if(!init){
+ kernel = get_kernel("src/convolutional_layer.cl", "learn_bias", 0);
+ init = 1;
+ }
+ return kernel;
+}
+
+void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
+{
+ int size = convolutional_out_height(layer) * convolutional_out_width(layer);
+
+ cl_setup();
+ cl_kernel kernel = get_convolutional_learn_bias_kernel();
+ cl_command_queue queue = cl.queue;
+
+ cl_uint i = 0;
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.batch), (void*) &layer.batch);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.delta_cl), (void*) &layer.delta_cl);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.bias_updates_cl), (void*) &layer.bias_updates_cl);
+ check_error(cl);
+
+ const size_t global_size[] = {layer.n};
+
+ clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+ check_error(cl);
+}
+
+cl_kernel get_convolutional_bias_kernel()
+{
+ static int init = 0;
+ static cl_kernel kernel;
+ if(!init){
+ kernel = get_kernel("src/convolutional_layer.cl", "bias", 0);
+ init = 1;
+ }
+ return kernel;
+}
+
+void bias_output_gpu(const convolutional_layer layer)
+{
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
+ int size = out_h*out_w;
+
+ cl_setup();
+ cl_kernel kernel = get_convolutional_bias_kernel();
+ cl_command_queue queue = cl.queue;
+
+ cl_uint i = 0;
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.biases_cl), (void*) &layer.biases_cl);
+ cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl);
+ check_error(cl);
+
+ const size_t global_size[] = {layer.batch, layer.n*size};
+
+ clEnqueueNDRangeKernel(queue, kernel, 2, 0, global_size, 0, 0, 0, 0);
+ check_error(cl);
+}
+
+//#define TIMEIT
+
void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in)
{
+ int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
int n = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.batch;
+ convolutional_out_width(layer);
- cl_write_array(layer.filters_cl, layer.filters, m*k);
- cl_mem a = layer.filters_cl;
- cl_mem b = layer.col_image_cl;
- cl_mem c = layer.output_cl;
- im2col_ongpu(in, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
- gemm_ongpu(0,0,m,n,k,1,a,k,b,n,0,c,n);
- activate_array_ongpu(layer.output_cl, m*n, layer.activation);
- cl_read_array(layer.output_cl, layer.output, m*n);
+ bias_output_gpu(layer);
+
+ #ifdef TIMEIT
+ clock_t time = clock();
+ printf("Forward\n");
+ #endif
+
+ im2col_ongpu(in, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
+
+ #ifdef TIMEIT
+ clFinish(cl.queue);
+ printf("Im2col %f\n", sec(clock()-time));
+ time = clock();
+ #endif
+
+ for(i = 0; i < layer.batch; ++i){
+ cl_mem a = layer.filters_cl;
+ cl_mem b = cl_sub_array(layer.col_image_cl, i*k*n, k*n);
+ cl_mem c = cl_sub_array(layer.output_cl, i*m*n, m*n);
+ gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c,n);
+ clReleaseMemObject(b);
+ clReleaseMemObject(c);
+ }
+ #ifdef TIMEIT
+ clFinish(cl.queue);
+ printf("Gemm %f\n", sec(clock()-time));
+ #endif
+ activate_array_ongpu(layer.output_cl, m*n*layer.batch, layer.activation);
+ #ifdef TIMEIT
+ cl_read_array(layer.output_cl, layer.output, m*n*layer.batch);
+ #endif
}
+
+void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem delta_cl)
+{
+ int i;
+ int m = layer.n;
+ int n = layer.size*layer.size*layer.c;
+ int k = convolutional_out_height(layer)*
+ convolutional_out_width(layer);
+ gradient_array_ongpu(layer.output_cl, m*k*layer.batch, layer.activation, layer.delta_cl);
+ learn_bias_convolutional_layer_ongpu(layer);
+
+ for(i = 0; i < layer.batch; ++i){
+ cl_mem a = cl_sub_array(layer.delta_cl,i*m*k, m*k);
+ cl_mem b = cl_sub_array(layer.col_image_cl,i*k*n, k*n);
+ cl_mem c = layer.filter_updates_cl;
+
+ gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+ clReleaseMemObject(a);
+ clReleaseMemObject(b);
+ }
+ //cl_read_array(layer.delta_cl, layer.delta, m*k*layer.batch);
+
+ if(delta_cl){
+ m = layer.size*layer.size*layer.c;
+ k = layer.n;
+ n = convolutional_out_height(layer)*
+ convolutional_out_width(layer);
+
+ for(i = 0; i < layer.batch; ++i){
+ cl_mem a = layer.filters_cl;
+ cl_mem b = cl_sub_array(layer.delta_cl, i*k*n, k*n);
+ cl_mem c = cl_sub_array(layer.col_image_cl, i*m*n, m*n);
+
+ gemm_ongpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
+ clReleaseMemObject(b);
+ clReleaseMemObject(c);
+ }
+
+ scal_ongpu(layer.batch*layer.h*layer.w*layer.c,0,delta_cl, 1);
+ col2im_ongpu(layer.col_image_cl, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_cl);
+ }
+}
+
+void pull_convolutional_layer(convolutional_layer layer)
+{
+ cl_read_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
+ cl_read_array(layer.biases_cl, layer.biases, layer.n);
+}
+
+void push_convolutional_layer(convolutional_layer layer)
+{
+ cl_write_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
+ cl_write_array(layer.biases_cl, layer.biases, layer.n);
+}
+
+void update_convolutional_layer_gpu(convolutional_layer layer)
+{
+ int size = layer.size*layer.size*layer.c*layer.n;
+ axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
+ scal_ongpu(layer.n,layer.momentum, layer.bias_updates_cl, 1);
+
+ scal_ongpu(size, 1.-layer.learning_rate*layer.decay, layer.filters_cl, 1);
+ axpy_ongpu(size, layer.learning_rate, layer.filter_updates_cl, 1, layer.filters_cl, 1);
+ scal_ongpu(size, layer.momentum, layer.filter_updates_cl, 1);
+ pull_convolutional_layer(layer);
+}
+
+
#endif
--
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